COMPARATIVE STUDY OF FEATURE SELECTION METHODS TO ANALYZE PERFORMANCE OF LUNG CANCER DATA


Koc E., Ozer A. N.

European Conference on Data Mining (ECDM) / International Conference on Intelligent Systems and Agents / International Conference on Theory and Practice in Modern Computing, Las-Palmas, İspanya, 22 - 24 Temmuz 2015, ss.219-222 identifier

  • Basıldığı Şehir: Las-Palmas
  • Basıldığı Ülke: İspanya
  • Sayfa Sayıları: ss.219-222

Özet

Feature selection, also known as attribute selection, is a process which attempts to select more informative features among datasets to be used in model construction. The main aim of feature selection can improve the prediction accuracy and reduce the computational overhead of classification algorithms. In this study, several approaches such as Information Gain Attribute Evaluation, Chi-Squared Attribute Evaluation, Filtered Attribute Evaluation, Gain Ratio Attribute Evaluation and Symmetrical Uncertainty Attribute Evaluation are carried out to discover the discriminative features on the same disease, namely lung cancer, using four different medical datasets. The efficiency of each approach is evaluated using machine learning software.